Jordan Can. Form of Frobenius map

In summary, the conversation discusses finding the Jordan canonical form for the Frobenius map on the finite field F_{p^n}. The speaker suggests using a basis of a^n to find the matrix of the map, which will be a permutation matrix. They also mention that the characteristic polynomial will have n distinct eigenvalues, resulting in an n-dimensional Jordan canonical form with the roots of unity on the diagonal. The speaker asks for confirmation of their approach or if there is a simpler method.
  • #1
geor
35
0
Hello all,

I am trying to solve this exercise here:

Let \phi denote the Frobenius map x |-> x^p on the finite field F_{p^n}. Determine the Jordan canonical form (over a field containing all the eigenvalues) for \phi considered as an F_p-linear transformation of the n-dimensional F_p-vector space F_{p^n}.

So, this is how I start:

Suppose that F_{p^n}=F_p(a1,a2,a3, ..., an) (those n elements will be powers of one element, but it doesn't matter). Now, since the Frobenius map is an isomorphism of F_{p^n} to itself, then \phi permutes a1, a2, ..., an.

Since a1, a2, ..., a3 form a basis of the n-dimensional F_p-vector space F_{p^n}, then the matrix of \phi in respect with that basis will be just a permutation matrix.

So the problem becomes equivalent with: "find the jordan canonical form of a permutation matrix".

Am I doing some obvious mistake here? Would the latter be something straightforward? I admit I can't see it...

Any help would be greatly appreciated.
 
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  • #2
Okay, so this is a similar way that seems to work for me:

Suppose F_{p^n}=F_p(a), where a is a root of some irreducible polynomial over F_p of degree n.

Then,
a^(p^n-1), ..., a^{p^2}, a^p, a (= a^{p^n}) is a basis of the F_p-vector space F_p(a)

Then we notice that \phi(a^{p^i}) = a^{p^i+1}

So, in respect to the basis above, the matrix of \phi
becomes:

0 1 0 0 0 ... 0 0
0 0 1 0 0 ... 0 0
0 0 0 1 0 ... 0 0
.....
.....
0 0 0 0 0 ... 1 0
0 0 0 0 0 ... 0 1
1 0 0 0 0 ... 0 0

With some little effort, one can see that the characteristic polynomial
of this matrix is (t^n)-1.

That is, we have n distinct eigenvalues (all the nth roots of unity).

Thus, we will have n Jordan blocks, meaning that the Jordan canonical form
will be the diagonal matrix with the roots of unity in the diagonal

Could somebody please tell me if my arguments are correct or I miss something?
Or, if the above are correct, is there a simpler way to obtain this result?

Thanks a lot..
 
Last edited:

Related to Jordan Can. Form of Frobenius map

1. What is the Jordan Canonical Form?

The Jordan Canonical Form is a way to represent a square matrix as a block diagonal matrix with Jordan blocks on the diagonal. It is useful in solving systems of linear equations and studying the behavior of linear transformations.

2. What is the Frobenius map?

The Frobenius map is a homomorphism from a finite field to itself, defined by raising each element to a power equal to the field's characteristic. It is an important tool in algebraic geometry and number theory.

3. What is the connection between the Jordan Canonical Form and the Frobenius map?

The connection between the Jordan Canonical Form and the Frobenius map lies in the fact that the Jordan blocks in the canonical form are precisely the orbits of the Frobenius map on the matrix. This connection is useful in understanding the behavior of the Frobenius map on matrices.

4. How is the Jordan Canonical Form of a matrix related to its eigenvalues?

The eigenvalues of a matrix can be found on the diagonal of its Jordan Canonical Form. Each eigenvalue appears on the diagonal as many times as its algebraic multiplicity, which is the number of times it appears as a root of the characteristic polynomial. The size and shape of the Jordan blocks also provide information about the eigenspace associated with each eigenvalue.

5. Can the Jordan Canonical Form always be computed for a given matrix?

Yes, the Jordan Canonical Form can always be computed for a square matrix over a field. However, the computation may be difficult or time-consuming for larger matrices.

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